Oracle9iAS Personalization helps companies provide real-time recommendations over the internet — supplying customers with personalized product recommendations, ratings of the likelihood that the customer will "like" the recommendations, and improved site navigation based on visitor interests and profiles.

• Delivering Personalized Web Visitor Recommendations
  
• Real-Time Recommendation Engine Architecture
  
• Automatic Customer Profiling and Modeling
  
• Oracle9iAS Personalization API (Applications Programming Interface)
  
• Single Administrative Interface
  
• Key Differentiators
  
• Additional Features
  
• Platform Requirements
  

Delivering Personalized Web Visitor Recommendations

Oracle9iAS Personalization is an option to Oracle9i Application Server — the industry's most complete and integrated application server — providing real-time personalization for e-business sales channels, such as Web Stores, application hosting environments, and call centers. Oracle9iAS Personalization provides an integrated real-time recommendation engine that is deployed via Oracle9i Application Server.

By delivering real-time personalization via Oracle9iAS and Oracle9i Database, Oracle9iAS Personalization delivers powerful, scalable real-time personalization for customer "touch points." This enables e-businesses to deliver tailored, 1:1 customer experiences that will turn browsers into buyers.

Oracle9iAS Personalization is designed to meet the challenges of vast amounts of Web data and yet enable the personal, 1:1 relationships that e-businesses require in order to compete today. Because it benefits from the scalability of Oracle9i, Oracle9iAS Personalization can analyze large volumes of customer data while preserving the uniqueness of individual customer relationships.

Oracle9iAS Personalization uses data mining technology to sift through the mountains of e-business data generated from customers' clicks, transactions, demographics, and ratings data gathered from Web sites.

Oracle9iAS Personalization provides real-time recommendations and answers to questions such as:

• Which items is this person most likely to buy or like?
  
• People that bought or like this item are likely to buy or like which other item(s)?
  
• How likely is this person to buy or like this item?
  
• Which items is this person most likely to buy or like given he likes or is buying another item?
  

E-commerce sites and Web portals can provide their e-business customers with personalized product recommendations, ratings of the likelihood that they will "like" the recommendations, and improved site navigation based on their interests and profiles.

Real-Time Recommendation Engine Architecture

Oracle9iAS Personalization allows e-businesses to personalize Web sites to each individual visitor, resulting in increased revenue and customer satisfaction. Oracle9iAS Personalization uses SQL queries for obtaining scores, which can be executed in real-time or batch mode.

Recommendation engines serve Oracle9iAS Personalization's real-time recommendations to Web sites across the enterprise.

Oracle9iAS Personalization's predictive models may be rebuilt on a periodic basis — e.g. daily, weekly, monthly — and deployed to the recommendation engines when they have completed.

Oracle9iAS Personalization allows users to create "recommendation engine farms" that are comprised of many recommendation engines serving customized recommendations to the Web site. This architecture is extremely scalable for high-traffic sites.

Oracle9iAS Personalization and Oracle9i Database store the predictive models in memory to handle the high traffic and speed requirements associated with e-commerce sites. Transactional Naïve Bayes and Predictive Association Rules data mining algorithms find hidden patterns and customer profiles that drive personalized recommendations.

Automatic Customer Profiling and Modeling

Oracle9iAS Personalization minimizes the effort needed to create highly accurate personalized recommendations.

Using data from multiple sources, including customer databases, clickstream data, and transaction systems, Oracle9iAS Personalization builds a real-time profile for each customer.

Oracle9iAS Personalization selects the best offer for each point of contact based on what it knows about a particular customer. As individuals accept or decline offers, Oracle9iAS Personalization adjusts and incorporates that information into future offers.

Oracle9iAS Personalization API (Applications Programming Interface)

The Oracle9iAS Personalization API allows e-businesses to offer real-time personalization to their registered customers and Web visitors for any Java Web site running on Oracle9iAS.

The Oracle9iAS Personalization API allows customers to instrument their Web sites to collect customer "click" data. This API eliminates the need to sift through mountains of noisy clickstream data.

Oracle9iAS Personalization's flexible and tunable recommendation API enables applications to deploy a variety of recommendation strategies. The API allows the application developer to specify various model-tuning parameters. Hence, the real-time recommendations can be tuned to support the needs of a variety of "customer touch points."

Single Administrative Interface

Oracle9iAS Personalization reduces routine maintenance efforts by allowing Web administrators to build, tailor, manage, and deploy many recommendation engines enterprise-wide from a single administrative interface.

Web administrators can also set up schedules for primary events — such as model building, model deployment, and reporting — to occur automatically. Additionally, they can schedule the deployment of multiple recommendation strategies for different campaigns or time (such as holiday) periods, or to capture and model behavior for specific events.

Key Differentiators

1. Real-Time Recommendation Engine Deployed on Oracle9iAS
2. Model Building Embedded in Oracle9i Database
3. Data Mining Technology

1. Real-Time Recommendation Engine Deployed on Oracle9iAS
   Oracle9iAS Personalization dynamically serves personalized recommendations (such as products, content, and navigational links) in real-time based on a registered customer's or anonymous visitor's explicit (transactions, purchases, ratings, and demographic data) and implicit (mouse clicks, pages visited, and banners viewed) information.
   • Handles Anonymous Visitors, "Sessions," and Navigational Data
     Oracle9iAS Personalization can make informed recommendations based upon implicit customer information (the pages visited, banners viewed, and mouse clicks). Oracle9iAS Personalization can deal with anonymous visitors because it tracks "sessions" and navigational data. It can take as input Web pages and banners visited and use that information to suggest recommendations or to improve site navigation. Oracle9iAS Personalization can also integrate with applications that do not have session management by creating its own session IDs to track visitor activity.
     • "Anonymous visitor" example: Recommend books about national parks and outdoor cooking to anonymous visitors who are currently viewing cycling and skiing Web pages

     • "Registered customer" example: Recommend home exercise equipment to people who bought sneakers and winter jackets
       
       

   • Single Administrative GUI
     Oracle9iAS Personalization allows you to build, tailor, manage, and deploy many recommendation engines enterprise-wide from a single administrative interface. Additionally, it supports scheduling the deployment of multiple recommendation strategies for different campaigns or time (such as holiday) periods, or to capture and model behavior for specific events, via an events scheduler.
     
     

2. Model Building Embedded in Oracle9i Database
   Oracle9iAS Personalization is completely embedded within the Oracle9i infrastructure, for power, scalability, and minimization of data redundancy.
   • Scalability
     Because it benefits from the scalability of Oracle9i, the world's most powerful database and application server for e-business, Oracle9iAS Personalization analyzes large volumes of customer data while preserving the uniqueness of individual customer relationships — delivering personalized recommendations in real-time.

   • Complete, Integrated Solution
     Oracle9iAS Personalization combines customer information from a variety of sources, reduces data movement and redundancy, and provides a 360-degree customer view to better understand and satisfy customer needs. Because this information is in Oracle9i Database, it is available for all other Oracle applications and users.
     
     

3. Data Mining Technology
   Powerful data mining technology embedded in Oracle9i Database automatically discovers individualized behavior patterns to generate highly accurate personalized recommendations in real-time.
   • Tunable
     Oracle9iAS Personalization provides access to advanced, tunable modeling and recommendation parameters via an API, for Java application developers.
     • Recency factor
       Oracle9iAS Personalization handles current session behavior separately from historic data, enabling a merchant to assign them different weights. In contrast, traditional collaborative filtering techniques cumulate implicit ratings over time — for example, a browsing session 3 years ago would be given the same weight as a current browsing session of the same visitor.

     • Personalization Index
       Oracle9iAS Personalization provides the ability to tune recommendations from the expected recommendations to "surprise" recommendations. Rather than always recommending the obvious, this allows Web sites to provide alternative recommendations that may be of more value to the customer. Personalization Index settings can be uniquely set for individual visitors or different areas of the Web site.

     • Current "session" vs. historical behavior
       Oracle9iAS Personalization offers the flexibility to weight recent activity more heavily than past purchases and to make other adjustments at the API level to provide fine-tuned recommendations for each visitor.
       
       

   • Automated
     Oracle9iAS Personalization provides automated predictive model building, model deployment, and performance reporting.

ADDITIONAL FEATURES

Data Access

• Any Web site that supports Oracle HTTP Server powered by Apache for Web data collection and real-time personalization

PLATFORM REQUIREMENTS

• Oracle9iAS Personalization runs on any supported Oracle9i Application Server

• Oracle9i Database (required)

• Oracle9i Partitioning (recommended)